CTREE, 1 June 2017
We stole a lot of this work wholesale from people who know more than we do. We thank them and ask their forgiveness.
Michael:
Senior thesis seminar with 9 students
Michael:
Advantages:
Cost
Aaron:
Please take a couple of minutes to write down a question you have about R Markdown and Reproducibility.
Think about the following ideas:
Just take a moment to write down something along these lines for now and put aside your notecard.
Courtesy of Bray, 2016
The Good
The Bad
The Ugly?
Courtesy of Bray, 2016
The good
The bad
The ugly?
# Header 1 ## Header 2 ### Header 3 This is normal sized text used in the body of our work. For bullet points, we use dashes, e.g. - Intro to RStudio - More content - a sub-point - Back to the original level
R Markdown can produce a variety of document types (other than the default html page):
pdf_document makes a PDF with LaTeX (.pdf)
word_document for Microsoft Word documents (.docx).
odt_document for OpenDocument Text documents (.odt).
rtf_document for Rich Text Format documents (.rtf)
And others.
R Markdown can also be re-purposed to produce a presentation file (as with this presentation):
io_slides opens in your browser and interactive (.html)
slidy another browser based presentation format (.html)
beamer makes a PDF with LaTeX (.pdf)
Think about data analysis as falling into three loose categories:
All of this occurs in the code "chunk"
To open a code chunk hit CMD + OPTION + I on a Mac
Or type out three backticks ``` folowed by {r}
And then three more back ticks ``` on another line.
Within the {r} you can specify options, like {eval = FALSE} if you don't want it to evaluate the code
Or you can label the code chunk, e.g. {r cars} labels the chunk "cars" in your ToC
```{r cars, echo = TRUE}
summary(cars)
```
The option echo = TRUE means that the code gets included in the rendered html.
summary(cars)
## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00
## session subject r1 r2 r3 r4 r5 r6 r7 r8 r9 treatment team ## 1 1 1 0 0 0 0 10 10 0 0 0 individual NA ## 2 1 2 0 0 30 40 40 0 0 0 20 individual NA ## 3 1 3 30 30 0 0 0 60 60 10 0 individual NA ## 4 1 4 20 0 100 0 0 30 75 100 100 individual NA ## 5 1 5 100 100 100 100 100 100 100 100 100 individual NA ## 6 1 6 100 100 100 100 100 100 100 0 0 individual NA ## uniqid ## 1 1_individual_1 ## 2 1_individual_2 ## 3 1_individual_3 ## 4 1_individual_4 ## 5 1_individual_5 ## 6 1_individual_6
## ## Wilcoxon rank sum test with continuity correction ## ## data: value by treatment ## W = 52876, p-value = 3.838e-10 ## alternative hypothesis: true location shift is not equal to 0
## ## Call: ## lm(formula = value ~ treatment, data = SutNarrow) ## ## Residuals: ## Min 1Q Median 3Q Max ## -61.370 -29.385 -0.542 38.630 60.615 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 39.385 1.451 27.152 < 2e-16 *** ## treatmentmessage 21.985 1.994 11.028 < 2e-16 *** ## treatmentmixed 10.609 1.925 5.510 3.92e-08 *** ## treatmentpaycomm 10.886 2.144 5.077 4.09e-07 *** ## treatmentteamtreat 16.313 2.629 6.204 6.34e-10 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 34.81 on 2713 degrees of freedom ## Multiple R-squared: 0.04473, Adjusted R-squared: 0.04333 ## F-statistic: 31.76 on 4 and 2713 DF, p-value: < 2.2e-16
## Oneway (time) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = value ~ treatment, data = SutNarrow, effect = "time",
## model = "random", index = c("uniqid"))
##
## Balanced Panel: n=302, T=9, N=2718
##
## Effects:
## var std.dev share
## idiosyncratic 1197.631 34.607 0.985
## time 18.044 4.248 0.015
## theta: 0.5755
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -61.9533 -28.6148 -2.6745 33.8010 64.4377
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) 39.3854 2.0207 19.4914 < 2.2e-16 ***
## treatmentmessage 21.9850 1.9815 11.0951 < 2.2e-16 ***
## treatmentmixed 10.6093 1.9137 5.5437 3.246e-08 ***
## treatmentpaycomm 10.8862 2.1313 5.1079 3.484e-07 ***
## treatmentteamtreat 16.3130 2.6134 6.2420 4.996e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 3402300
## Residual Sum of Squares: 3248300
## R-Squared: 0.045255
## Adj. R-Squared: 0.043848
## F-statistic: 32.1494 on 4 and 2713 DF, p-value: < 2.22e-16
| Dependent variable: | |
| value | |
| treatmentmessage | 21.985*** |
| (1.994) | |
| treatmentmixed | 10.609*** |
| (1.925) | |
| treatmentpaycomm | 10.886*** |
| (2.144) | |
| treatmentteamtreat | 16.313*** |
| (2.629) | |
| Constant | 39.385*** |
| (1.451) | |
| Observations | 2,718 |
| R2 | 0.045 |
How about Bayes' Rule?
\[Pr(\mbox{Outcome} | \mbox{signal}) = \frac{\theta p}{\theta p - (1 - \theta)(1 - p)}\]
R Markdown uses \(\LaTeX\) for math and it immediately gets displayed in R Studio.
That is, \(\LaTeX\) without the challenges of learning the packages, tables, etc that makes learning \(\LaTeX\) so hard.
In-line equations are bracketed by single dollar signs $.
Off-set equations are bracketed by double dollar signs $$.
R Markdown and R Studio together have excellent capabilities.
Michael:
Students will only learn commands through graded assignments
Aaron:
Students can struggle with basic computing (working directory, etc.)
New to R?